Summary
Nitrous oxide (N2O) is emitted during microbiological nitrogen (N) conversion processes, when N2O production exceeds N2O consumption. The magnitude of N2O production vs. consumption varies ...with pH and controlling net N2O production might be feasible by choice of system pH. This article reviews how pH affects enzymes, pathways and microorganisms that are involved in N‐conversions in water engineering applications. At a molecular level, pH affects activity of cofactors and structural elements of relevant enzymes by protonation or deprotonation of amino acid residues or solvent ligands, thus causing steric changes in catalytic sites or proton/electron transfer routes that alter the enzymes' overall activity. Augmenting molecular information with, e.g., nitritation or denitrification rates yields explanations of changes in net N2O production with pH. Ammonia oxidizing bacteria are of highest relevance for N2O production, while heterotrophic denitrifiers are relevant for N2O consumption at pH > 7.5. Net N2O production in N‐cycling water engineering systems is predicted to display a ‘bell‐shaped’ curve in the range of pH 6.0–9.0 with a maximum at pH 7.0–7.5. Net N2O production at acidic pH is dominated by N2O production, whereas N2O consumption can outweigh production at alkaline pH. Thus, pH 8.0 may be a favourable pH set‐point for water treatment applications regarding net N2O production.
Cell-type heterogeneity of tumors is a key factor in tumor progression and response to chemotherapy. Tumor cell-type heterogeneity, defined as the proportion of the various cell-types in a tumor, can ...be inferred from DNA methylation of surgical specimens. However, confounding factors known to associate with methylation values, such as age and sex, complicate accurate inference of cell-type proportions. While reference-free algorithms have been developed to infer cell-type proportions from DNA methylation, a comparative evaluation of the performance of these methods is still lacking.
Here we use simulations to evaluate several computational pipelines based on the software packages MeDeCom, EDec, and RefFreeEWAS. We identify that accounting for confounders, feature selection, and the choice of the number of estimated cell types are critical steps for inferring cell-type proportions. We find that removal of methylation probes which are correlated with confounder variables reduces the error of inference by 30-35%, and that selection of cell-type informative probes has similar effect. We show that Cattell's rule based on the scree plot is a powerful tool to determine the number of cell-types. Once the pre-processing steps are achieved, the three deconvolution methods provide comparable results. We observe that all the algorithms' performance improves when inter-sample variation of cell-type proportions is large or when the number of available samples is large. We find that under specific circumstances the methods are sensitive to the initialization method, suggesting that averaging different solutions or optimizing initialization is an avenue for future research.
Based on the lessons learned, to facilitate pipeline validation and catalyze further pipeline improvement by the community, we develop a benchmark pipeline for inference of cell-type proportions and implement it in the R package medepir.
Taste is essential for the interaction of animals with their food and has co-evolved with diet. Humans have peopled a large range of environments and present a wide range of diets, but little is ...known about the diversity and evolution of human taste perception. We measured taste recognition thresholds across populations differing in lifestyles (hunter gatherers and farmers from Central Africa, nomad herders, and farmers from Central Asia). We also generated genome-wide genotype data and performed association studies and selection scans in order to link the phenotypic variation in taste sensitivity with genetic variation. We found that hunter gatherers have lower overall sensitivity as well as lower sensitivity to quinine and fructose than their farming neighbors. In parallel, there is strong population divergence in genes associated with tongue morphogenesis and genes involved in the transduction pathway of taste signals in the African populations. We find signals of recent selection in bitter taste-receptor genes for all four populations. Enrichment analysis on association scans for the various tastes confirmed already documented associations and revealed novel GO terms that are good candidates for being involved in taste perception. Our framework permitted us to gain insight into the genetic basis of taste sensitivity variation across populations and lifestyles.
An external control arm is a cohort of control patients that are collected from data external to a single-arm trial. To provide an unbiased estimation of efficacy, the clinical profiles of patients ...from single and external arms should be aligned, typically using propensity score approaches. There are alternative approaches to infer efficacy based on comparisons between outcomes of single-arm patients and machine-learning predictions of control patient outcomes. These methods include G-computation and Doubly Debiased Machine Learning (DDML) and their evaluation for External Control Arms (ECA) analysis is insufficient.
We consider both numerical simulations and a trial replication procedure to evaluate the different statistical approaches: propensity score matching, Inverse Probability of Treatment Weighting (IPTW), G-computation, and DDML. The replication study relies on five type 2 diabetes randomized clinical trials granted by the Yale University Open Data Access (YODA) project. From the pool of five trials, observational experiments are artificially built by replacing a control arm from one trial by an arm originating from another trial and containing similarly-treated patients.
Among the different statistical approaches, numerical simulations show that DDML has the smallest bias followed by G-computation. In terms of mean squared error, G-computation usually minimizes mean squared error. Compared to other methods, DDML has varying Mean Squared Error performances that improves with increasing sample sizes. For hypothesis testing, all methods control type I error and DDML is the most conservative. G-computation is the best method in terms of statistical power, and DDML has comparable power at Formula: see text but inferior ones for smaller sample sizes. The replication procedure also indicates that G-computation minimizes mean squared error whereas DDML has intermediate performances in between G-computation and propensity score approaches. The confidence intervals of G-computation are the narrowest whereas confidence intervals obtained with DDML are the widest for small sample sizes, which confirms its conservative nature.
For external control arm analyses, methods based on outcome prediction models can reduce estimation error and increase statistical power compared to propensity score approaches.
Finding genetic signatures of local adaptation is of great interest for many population genetic studies. Common approaches to sorting selective loci from their genomic background focus on the extreme ...values of the fixation index, FST, across loci. However, the computation of the fixation index becomes challenging when the population is genetically continuous, when predefining subpopulations is a difficult task, and in the presence of admixed individuals in the sample. In this study, we present a new method to identify loci under selection based on an extension of the FST statistic to samples with admixed individuals. In our approach, FST values are computed from the ancestry coefficients obtained with ancestry estimation programs. More specifically, we used factor models to estimate FST, and we compared our neutrality tests with those derived from a principal component analysis approach. The performances of the tests were illustrated using simulated data and by re‐analysing genomic data from European lines of the plant species Arabidopsis thaliana and human genomic data from the population reference sample, POPRES.
Two competing hypotheses are at the forefront of the debate on modern human origins. In the first scenario, known as the recent Out-of-Africa hypothesis, modern humans arose in Africa about ...100,000-200,000 years ago and spread throughout the world by replacing the local archaic human populations. By contrast, the second hypothesis posits substantial gene flow between archaic and emerging modern humans. In the last two decades, the young time estimates--between 100,000 and 200,000 years--of the most recent common ancestors for the mitochondrion and the Y chromosome provided evidence in favor of a recent African origin of modern humans. However, the presence of very old lineages for autosomal and X-linked genes has often been claimed to be incompatible with a simple, single origin of modern humans. Through the analysis of a public DNA sequence database, we find, similar to previous estimates, that the common ancestors of autosomal and X-linked genes are indeed very old, living, on average, respectively, 1,500,000 and 1,000,000 years ago. However, contrary to previous conclusions, we find that these deep gene genealogies are consistent with the Out-of-Africa scenario provided that the ancestral effective population size was approximately 14,000 individuals. We show that an ancient bottleneck in the Middle Pleistocene, possibly arising from an ancestral structured population, can reconcile the contradictory findings from the mitochondrion on the one hand, with the autosomes and the X chromosome on the other hand.
The arts are becoming a favored medium for conveying science to the public. Tracking trending approaches, such as community-engaged learning, alongside challenges and goals can help establish metrics ...to achieve more impactful outcomes, and to determine the effectiveness of arts-based science communication for raising awareness or shaping public policy.
Randomized clinical trials (RCTs) are the gold standard in producing clinical evidence of efficacy and safety of medical interventions. More recently, a new paradigm is emerging—specifically within ...the context of preauthorization regulatory decision‐making—for some novel uses of real‐world evidence (RWE) from a variety of real‐world data (RWD) sources to answer certain clinical questions. Traditionally reserved for rare diseases and other special circumstances, external controls (eg, historical controls) are recognized as a possible type of control arm for single‐arm trials. However, creating and analyzing an external control arm using RWD can be challenging since design and analytics may not fully control for all systematic differences (biases). Nonetheless, certain biases can be attenuated using appropriate design and analytical approaches. The main objective of this paper is to improve the scientific rigor in the generation of external control arms using RWD. Here we (a) discuss the rationale and regulatory circumstances appropriate for external control arms, (b) define different types of external control arms, and (c) describe study design elements and approaches to mitigate certain biases in external control arms. This manuscript received endorsement from the International Society for Pharmacoepidemiology (ISPE).
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